CN112732766B - Data sorting method and device, electronic equipment and storage medium - Google Patents
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Abstract
The invention discloses a data sorting method, a device, electronic equipment and a storage medium, wherein the method comprises the following steps: when a data ordering request is received, determining the membership degree and non-membership degree of each attribute of each piece of data in the system; determining first target role information and target login time information of a user currently logged in the system, and determining target attribute sequencing according to a corresponding relation between each login time period and attribute sequencing, which is pre-stored for each role information; and sequencing each piece of data according to the membership degree and non-membership degree of each attribute of each piece of data, the target attribute sequencing and the intuitionistic fuzzy decision method. The embodiment of the invention can determine the data sequencing results corresponding to different users at different login times, thereby providing a data sequencing scheme based on the use habit of the users.
Description
Technical Field
The present invention relates to the field of data processing technologies, and in particular, to a data sorting method, a data sorting device, an electronic device, and a storage medium.
Background
The popularity of computers has been deepened into our daily life, entertainment and work, and each system in the computer contains various data, which becomes huge with the increase of the use time, and the important data actually needed to be known by the user is usually displayed in the form of a data table. Data sheets provide an important data visualization technique that can provide data in a very compact format and can provide access to a wide audience through proper design decisions.
Most of the current data table sorting methods sort through single characteristics such as data creation time and names, when the data volume is huge, the sorting according to the single characteristics can not clearly provide the user with the wanted data in time, and the process of searching the data by the user becomes very long. When the data size is large and the user finds the required data when the user is not sure to find the keywords of the data, a data sorting scheme based on the use habit of the user is needed.
Disclosure of Invention
The embodiment of the invention provides a data sorting method, a device, electronic equipment and a storage medium, which are used for providing a data sorting scheme based on the use habit of a user.
The embodiment of the invention provides a data ordering method, which comprises the following steps:
when a data ordering request is received, determining the membership degree and non-membership degree of each attribute of each piece of data in the system;
Determining first target role information and target login time information of a user currently logged in the system, and determining target attribute sequencing according to a corresponding relation between each login time period and attribute sequencing, which is pre-stored for each role information;
and sequencing each piece of data according to the membership degree and non-membership degree of each attribute of each piece of data, the target attribute sequencing and the intuitionistic fuzzy decision method.
Further, each attribute of the data includes at least one of:
Data newly-built time, data save time, data reference other data times, data reference other data weight values, data filling integrity, data name similarity, created data role information, data edit times, data latest edit time, data check times and data latest check time.
Further, determining the membership and non-membership of each attribute of the data includes:
If the data attribute is the data new time, acquiring the new time of the data, and determining the membership of the data according to the new time of the data and the preset corresponding relation between each new time period and the membership;
If the data attribute is data preservation time, acquiring new time of the data, determining preservation time of the data according to the new time and current time of the data, and determining membership of the data according to the preservation time of the data and a preset corresponding relation between each preservation time period and membership;
if the data attribute is the number of times that the data references other data, acquiring the number of times that the data references other data, and determining the membership degree of the data according to the number of times that the data references other data and the preset corresponding relation between each frequency range and the membership degree;
If the data attribute is the weight value of the data reference other data, determining the weight value of the other data of the data according to the preset weight value corresponding to each data, and determining the membership of the data according to the weight value of the data reference other data and the preset corresponding relation between each weight value range and the membership;
If the data attribute is the data filling integrity, determining the filling integrity of the data according to the filling condition of the data, and taking the filling integrity of the data as the membership of the data;
if the data attribute is the data name similarity, determining the name similarity of the data and other data, and taking the highest name similarity as the membership of the data;
If the data attribute is creation data role information, acquiring first target role information of a user currently logged in the system and creating second target role information of the data, and determining membership of the data according to the first target role information and the second target role information; if the first target role information and the second target role information are the same, determining that the membership degree of the data is highest, and if the first target role information and the second target role information are different, the higher the level of the second target role information is, the higher the membership degree of the data is;
If the data attribute is the data editing frequency, determining the editing frequency of the data, and determining the membership of the data according to the editing frequency of the data and the preset corresponding relation between each editing frequency range and the membership;
If the data attribute is the latest editing time of the data, acquiring the latest editing time of the data, and determining the membership of the data according to the latest editing time of the data and the preset corresponding relation between each editing time period and the membership;
if the data attribute is the data checking frequency, acquiring the checking frequency of the data, and determining the membership of the data according to the checking frequency of the data and the preset corresponding relation between each checking frequency range and the membership;
if the data attribute is the latest viewing time of the data, acquiring the latest viewing time of the data, and determining the membership of the data according to the latest viewing time of the data and the preset corresponding relation between each viewing time period and the membership;
For each data attribute, taking the difference value of the membership degree of 1 and the data as the non-membership degree of the data.
Further, after the data ordering request is received, before determining the membership degree and the non-membership degree of each attribute of each piece of data in the system, the method further includes:
judging whether the data ordering request carries a keyword or not, and if not, carrying out the follow-up steps.
Further, if the data ordering request carries a keyword, the method further includes:
According to an edit distance algorithm, determining the similarity between each piece of data in the system and the keywords, selecting target data with the similarity larger than a preset similarity threshold, and sorting according to the similarity of the target data.
Further, the method further comprises:
Judging whether target data with the same similarity exist or not, if so, determining the membership degree and non-membership degree of each attribute of the target data aiming at each piece of target data with the same similarity; and carrying out the subsequent sorting step according to the membership degree and non-membership degree of each attribute of each item of target data.
In another aspect, an embodiment of the present invention provides a data sorting apparatus, including:
the first determining module is used for determining the membership degree and the non-membership degree of each attribute of each piece of data in the system when a data ordering request is received;
The second determining module is used for determining first target role information and target login time information of a user currently logged in the system, and determining target attribute sequencing according to the corresponding relation between each login time period and attribute sequencing, which is pre-stored for each role information;
and the first ordering module is used for ordering each piece of data according to the membership degree and non-membership degree of each attribute of each piece of data, the target attribute ordering and the intuitionistic fuzzy decision method.
Further, the apparatus further comprises:
The judging module is used for judging whether the data ordering request carries keywords or not, and if not, triggering the first determining module.
Further, the apparatus further comprises:
The second sorting module is used for determining the similarity between each piece of data in the system and the keywords according to an edit distance algorithm, selecting target data with the similarity larger than a preset similarity threshold value, and sorting according to the similarity of the target data.
Further, the judging module is further configured to judge whether there is target data with the same similarity, and if so, trigger the first determining module for each piece of target data with the same similarity.
In yet another aspect, an embodiment of the present invention provides an electronic device, including a processor, a communication interface, a memory, and a communication bus, where the processor, the communication interface, and the memory complete communication with each other through the communication bus;
A memory for storing a computer program;
A processor for implementing any of the method steps described above when executing a program stored on a memory.
In yet another aspect, embodiments of the present invention provide a computer-readable storage medium having a computer program stored therein, which when executed by a processor, implements the method steps of any of the above.
The embodiment of the invention provides a data ordering method, a device, electronic equipment and a storage medium, wherein the method comprises the following steps: when a data ordering request is received, determining the membership degree and non-membership degree of each attribute of each piece of data in the system; determining first target role information and target login time information of a user currently logged in the system, and determining target attribute sequencing according to a corresponding relation between each login time period and attribute sequencing, which is pre-stored for each role information; and sequencing each piece of data according to the membership degree and non-membership degree of each attribute of each piece of data, the target attribute sequencing and the intuitionistic fuzzy decision method.
The technical scheme has the following advantages or beneficial effects:
Because in the embodiment of the invention, after receiving the data ordering request, the membership degree and non-membership degree of each attribute of each piece of data in the system can be determined, the target attribute ordering is determined according to the first target role information and the target login time information of the user currently logged in the system, and each piece of data is ordered according to the membership degree and non-membership degree of each attribute of each piece of data, the target attribute ordering and the intuitive fuzzy decision method. Therefore, the data sorting results corresponding to different users at different login times can be determined, and a data sorting scheme based on the use habit of the users is provided.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the description of the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic diagram of a data sorting process according to embodiment 1 of the present invention;
fig. 2 is a schematic diagram of time slot division provided in embodiment 2 of the present invention;
FIG. 3 is a block diagram of a data sorting system according to embodiment 3 of the present invention;
Fig. 4 is a schematic diagram of user role division provided in embodiment 3 of the present invention;
fig. 5 is a schematic structural diagram of a data sorting device according to embodiment 4 of the present invention;
Fig. 6 is a schematic structural diagram of an electronic device according to embodiment 5 of the present invention.
Detailed Description
The present invention will be described in further detail below with reference to the attached drawings, wherein it is apparent that the embodiments described are only some, but not all embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Example 1:
Fig. 1 is a schematic diagram of a data sorting process according to an embodiment of the present invention, including the following steps:
s101: when a data ordering request is received, for each piece of data in the system, the membership and non-membership of each attribute of the data are determined.
S102: and determining first target role information and target login time information of a user currently logged in the system, and determining target attribute sequencing according to the corresponding relation between each login time period and attribute sequencing, which are pre-stored for each role information.
S103: and sequencing each piece of data according to the membership degree and non-membership degree of each attribute of each piece of data, the target attribute sequencing and the intuitionistic fuzzy decision method.
The data sorting method provided by the embodiment of the invention is applied to electronic equipment, and the electronic equipment can be PC, tablet personal computer and other equipment.
After receiving the data sorting request, the electronic equipment acquires the membership degree and the non-membership degree of each attribute of each piece of data in the system, wherein each attribute of the data comprises at least one of the following: data newly-built time, data save time, data reference other data times, data reference other data weight values, data filling integrity, data name similarity, created data role information, data edit times, data latest edit time, data check times and data latest check time. And the electronic equipment can determine the first target role information of the user of the current login system according to the user name of the current login system, wherein the target role information comprises an administrator, an operator, a super administrator and the like. The electronic device can store role information corresponding to each user name, and when the user logs in the system, the first target role information of the user logging in the system can be determined according to the user name of the current logging in system. In addition, when a user logs into the system, the electronic device may identify target login time information for the user to log into the system.
The electronic equipment pre-stores the corresponding relation between each login time period and the attribute sequencing aiming at each role information, so that the target attribute sequencing can be determined according to the first target role information and the target login time information of the user currently logged in the system and the corresponding relation between each login time period and the attribute sequencing pre-stored aiming at each role information. After the electronic equipment determines the membership degree and non-membership degree of each attribute of each piece of data and the target attribute sequencing, the sequencing of each piece of data is completed according to an intuitive fuzzy decision method.
Because in the embodiment of the invention, after receiving the data ordering request, the membership degree and non-membership degree of each attribute of each piece of data in the system can be determined, the target attribute ordering is determined according to the first target role information and the target login time information of the user currently logged in the system, and each piece of data is ordered according to the membership degree and non-membership degree of each attribute of each piece of data, the target attribute ordering and the intuitive fuzzy decision method. Therefore, the data sorting results corresponding to different users at different login times can be determined, and a data sorting scheme based on the use habit of the users is provided.
Example 2:
On the basis of the above embodiment, in the embodiment of the present invention, determining the membership and non-membership of each attribute of the data includes:
If the data attribute is the data new time, acquiring the new time of the data, and determining the membership of the data according to the new time of the data and the preset corresponding relation between each new time period and the membership;
If the data attribute is data preservation time, acquiring new time of the data, determining preservation time of the data according to the new time and current time of the data, and determining membership of the data according to the preservation time of the data and a preset corresponding relation between each preservation time period and membership;
if the data attribute is the number of times that the data references other data, acquiring the number of times that the data references other data, and determining the membership degree of the data according to the number of times that the data references other data and the preset corresponding relation between each frequency range and the membership degree;
If the data attribute is the weight value of the data reference other data, determining the weight value of the other data of the data according to the preset weight value corresponding to each data, and determining the membership of the data according to the weight value of the data reference other data and the preset corresponding relation between each weight value range and the membership;
If the data attribute is the data filling integrity, determining the filling integrity of the data according to the filling condition of the data, and taking the filling integrity of the data as the membership of the data;
if the data attribute is the data name similarity, determining the name similarity of the data and other data, and taking the highest name similarity as the membership of the data;
If the data attribute is creation data role information, acquiring first target role information of a user currently logged in the system and creating second target role information of the data, and determining membership of the data according to the first target role information and the second target role information; if the first target role information and the second target role information are the same, determining that the membership degree of the data is highest, and if the first target role information and the second target role information are different, the higher the level of the second target role information is, the higher the membership degree of the data is;
If the data attribute is the data editing frequency, determining the editing frequency of the data, and determining the membership of the data according to the editing frequency of the data and the preset corresponding relation between each editing frequency range and the membership;
If the data attribute is the latest editing time of the data, acquiring the latest editing time of the data, and determining the membership of the data according to the latest editing time of the data and the preset corresponding relation between each editing time period and the membership;
if the data attribute is the data checking frequency, acquiring the checking frequency of the data, and determining the membership of the data according to the checking frequency of the data and the preset corresponding relation between each checking frequency range and the membership;
if the data attribute is the latest viewing time of the data, acquiring the latest viewing time of the data, and determining the membership of the data according to the latest viewing time of the data and the preset corresponding relation between each viewing time period and the membership;
For each data attribute, taking the difference value of the membership degree of 1 and the data as the non-membership degree of the data.
The following is an example:
And (3) data new time: in the unit of hours, if the system is an in-company system, according to fig. 2, 24 hours a day is divided into 12 equal parts, each equal part represents a period of time, and according to the hours of staff going to and from the company, a general user creates data in the case of between period 5 and period 10, the occupied ratio T 1 of all data in the system between eight in the morning and eight in the evening is calculated, and since staff is less likely to newly create data in between period 1 and period 3, the occupied ratio of the newly created data in the period is T 2, and the occupied ratio of the rest between period 4 and periods 11 and 12 is T 3, wherein T 1+T2+T3＝1;T1>T3>T2. The T 1、T2、T3 is the membership degree of the data.
Data retention time: taking a day as a unit, subtracting the newly built time from the current time when updating the sorting table each time to obtain a result of one day x, setting the use data probability of the day x which is less than or equal to 20 as D 1, setting the use data probability of the day x which is more than or equal to 100 as D 2, and setting the use data summary of the days between 20< x <100 as D 3, wherein D 1+D2+D3＝1;D1>D3>D2; the D 1、D2、D3 is the membership degree of the data.
Data references other data times: obtaining the number y of times of referring to other data, if the total number of the data can be referred to is X pieces of other data, setting different intervals according to the system condition, using the probability Q 1 when the reference number is 80%. Times.X.ltoreq.y, using the probability Q 2 when the reference number is y.ltoreq.10%. Times.X, and using the probability Q 3 when the reference number is 10%. Times.X < y < 80%. Times.X, wherein Q 1+Q2+Q3＝1;Q1>Q3>Q2; the Q 1、Q2、Q3 is the membership degree of the data.
Reference is made to other data weight values: obtaining reference other data, setting a weight value for each other data in the system, if the total weight value of the data which can reference the other data is Y, the weight value of the data which references the other data is C, setting different intervals according to the system condition, using the probability of C 1 when the reference weight is 80%. Ltoreq.c, using the probability of C 2 when the reference weight is c.ltoreq.10%. Ltoreq.Y, and using the probability of C 3 when the reference weight is 10%. Ltoreq.Y < C < 80%. Ltoreq.Y, wherein C 1+C2+C3＝1;C1>C3>C2); and C 1、C2、C3 is the membership degree of the data.
Data filling integrity: and calculating the data filling completeness F 1;F1 according to the filling condition of the data, namely, the membership degree of the data.
Similarity of data names: and obtaining the similarity percentages of the current data name and other data names by adopting an edit distance algorithm (LEVENSHTEIN DISTANCE), and finally obtaining the highest percentage N 1;N1 of the percentages to be the membership degree of the data. The similarity percentage of the current data name and other data names may be the similarity percentage of the current data name and the data names of other unreferenced pages.
Creating data role information: the roles of system users in the system are mainly divided into super administrator, manager and operator, the role CR of the current login user is obtained, the user role DR for creating the data is obtained, and if CR=DR, the use probability R 1 of the data is 1; when cd+.dr, if DR is a super administrator, the data usage probability is R 1, if DR is an administrator, the data usage probability is R 2, and if DR is an operator, the data usage probability is R 3;1>R1>R2>R3; r 1、R2、R3 is the membership degree of the data.
Number of data edits: obtaining the data editing times n, setting different intervals according to the system condition, wherein the use probability is E 1 when n is less than or equal to 5, E 2 when n is less than or equal to 1, and E 3;1>E1>E3>E2 when the reference number is 1< y < 5; e 1、E2、E3 is the membership degree of the data.
Recent edit time: taking a day as a unit, when updating the sorting table each time, subtracting the latest editing time (when the editing frequency is 0, namely new time) from the current time to obtain a result of one day m, setting the probability of using data of the day m which is less than or equal to 20 as DT 1, setting the probability of using data of the day m which is more than or equal to 100 as DT 2, and setting the summary of using data of the day between 20< m <100 as DT 3, wherein DT 1+DT2+DT3＝1;DT1>DT3>DT2; the DT 1、DT2、DT3 is the membership of the data.
Number of data views: obtaining the number i of the data checking detailed content, setting different intervals according to the system condition, wherein the use probability is CD 1 when i is not more than 5, the use probability is CD 2 when i is not more than 1, and the use probability is CD 3;1>CD1>CD3>CD2 when the reference number is 1< i < 5; the CD 1、CD2、CD3 is the membership of the data.
Data last view time: taking a day as a unit, when updating the sorting table each time, subtracting the time of the latest viewing detailed content from the current time (when the number of times of the latest viewing detailed content is 0, namely, the newly built time), obtaining a result of one day j, setting the probability of using data of the day j which is less than or equal to 20 as CDT 1, setting the probability of using data of the day j which is more than or equal to 100 as CDT 2, and setting the summary of using data of the day which is between 20< j <100 as CDT 3, wherein CDT 1+CDT2+CDT3＝1;CDT1>CDT3>CDT2; the CDT 1、CDT2、CDT3 is the membership of the data.
For each data attribute, taking the difference value of the membership degree of 1 and the data as the non-membership degree of the data.
The membership and non-membership of each attribute of the data is shown in the following table:
example 3:
on the basis of the foregoing embodiments, in an embodiment of the present invention, after the data ordering request is received, before determining, for each piece of data in the system, a membership degree and a non-membership degree of each attribute of the data, the method further includes:
judging whether the data ordering request carries a keyword or not, and if not, carrying out the follow-up steps.
In the embodiment of the invention, after receiving a data ordering request, the electronic device firstly judges whether a keyword is carried in the data ordering request before determining the membership degree and the non-membership degree of each attribute of each piece of data in the system, and if the keyword is not carried, the electronic device performs the subsequent steps of determining the membership degree and the non-membership degree of each attribute of each piece of data in the system and the subsequent ordering process according to each piece of data in the system.
If the data ordering request carries a keyword, the method further comprises:
According to an edit distance algorithm, determining the similarity between each piece of data in the system and the keywords, selecting target data with the similarity larger than a preset similarity threshold, and sorting according to the similarity of the target data.
In the embodiment of the invention, if the data ordering request carries the keyword, the electronic equipment determines the similarity between each piece of data in the system and the keyword according to the edit distance algorithm. And storing a preset similarity threshold in the electronic equipment, selecting target data with similarity larger than the preset similarity threshold, and sorting according to the similarity of the target data. The target data is generally sorted in order of similarity from large to small.
In order to further make the data sorting more accurate, in an embodiment of the present invention, the method further includes:
Judging whether target data with the same similarity exist or not, if so, determining the membership degree and non-membership degree of each attribute of the target data aiming at each piece of target data with the same similarity; and carrying out the subsequent sorting step according to the membership degree and non-membership degree of each attribute of each item of target data.
The electronic equipment determines the similarity between each piece of data and the keywords in the system according to the edit distance algorithm, then judges whether target data with the same similarity exist, and determines the membership degree and non-membership degree of each attribute of the target data aiming at the target data with the same similarity if the target data with the same similarity exist; and carrying out the subsequent sorting step according to the membership degree and non-membership degree of each attribute of each item of target data.
That is, the data sorting method provided by the embodiment of the invention comprises a data sorting method without keywords and a sorting method with keywords. Specifically, the keyword-free sorting method sorts each piece of data according to the membership degree and non-membership degree of each attribute of each piece of data, the target attribute sorting and the intuitionistic fuzzy decision method. The ordering method of the related keywords comprises the steps of determining the similarity between each piece of data in the system and the keywords according to an edit distance algorithm, selecting target data with the similarity larger than a preset similarity threshold value, and ordering according to the similarity of the target data. And in the ordering process of the keywords, if the target data with the same similarity exists, the target data with the same similarity is ordered by adopting an ordering method without the keywords, so that the data ordering is more accurate.
The embodiment of the invention aims to provide a sorting method for matching user requirements under a large number of data tables, which can effectively improve the reliability and accuracy of data sorting under the condition of no keywords.
Aiming at the purposes, the technical scheme adopted by the embodiment of the invention is as follows:
(1) The context information acquisition module acquires context information of a user:
The context information acquisition module records user context information, including user role information and login time information, the user roles are mainly divided into super administrators, administrators and operators, the obtained information is temporarily stored in the step (4), and data characteristics and ordering modes are determined according to the two context information.
(2) The operation module selects specific operation behaviors of a user in the system and responds differently according to the operation:
The operation module comprises four operation behaviors of newly-built data, edited data, checked data and search ordering data, wherein the search ordering data is divided into a related keyword search ordering and an unrelated keyword search ordering, when a user inputs keywords to search the data, the behavior is the related keyword search ordering, and when the user directly enters a data table page without searching the keywords, the behavior is the unrelated keyword search ordering.
(3) The feature extraction module extracts feature values from the data table and the user operation behaviors:
(3a) Extracting characteristic attributes from the newly built data behaviors and the newly built data acquired in the step (2);
(3b) The characteristic attribute is extracted again according to the new data after modification, the editing times and the latest editing time;
(3c) The detailed content behavior of the checked data obtained in the step (2) is extracted again according to the number of times of checking the detailed content of the data and the time of the latest checked detailed content of the data;
(4) Storing data into a database:
Storing the characteristic attribute obtained in the step (3) into a database XX1.db, and storing the corresponding conventional filling data into a database XX2.db;
(5) The ordering module orders the data table according to the user's needs:
And inputting the context information of the user login system and the search behavior of whether the user has keywords or not into a sequencing module according to the context information of the user login system, and obtaining a sequencing result.
As described above, the automatic sorting method for matching user requirements under a large number of data tables according to the embodiment of the invention has the following advantages: 1) An automatic ordering method model provided according to the user requirement under the condition of a large amount of data is designed, so that the user can quickly and accurately find the required content when the user is in a large amount of data; 2) The feature extraction mode aiming at a large amount of data is designed, a large amount of redundant features are removed, so that the matching speed is higher, and the response capability after the operation behavior is improved.
Embodiments of the invention are described in further detail below with reference to the attached drawing figures:
Fig. 3 is a block diagram of a data sorting system according to an embodiment of the present invention, including a PC device and a server of a user.
The user comprises at the PC device: and the contextual information acquisition module and the operation module.
Context information acquisition module: collecting context information of user login, including user role information and login time information;
And an operation module: the method comprises the operations of creating data by a user, editing the data, checking the data and searching the ordered data, wherein the searching of the ordered data is divided into searching with keywords and searching with irrelevant keywords;
the server comprises: database, extract characteristic module, sequencing module.
Database: storing three data modes, namely conventionally creating new data content, characteristic data corresponding to each piece of data and historical context information of a user;
And (3) extracting a characteristic module: the method mainly aims at extracting corresponding features of data generated by three operations of creating, editing and viewing detailed data of a user, and storing the three features in a database;
And a sequencing module: and determining irrelevant key words according to the operation of searching the ordering data of the user, and then matching the context information logged in by the current user with the data in the database to obtain an ordering result.
The context information acquisition module is specifically as follows:
And after the user successfully logs in the system, acquiring the current user role information and login time. As shown in fig. 4, the current user roles are largely divided into super manager, manager and operator; as shown in fig. 2, 24 hours a day is divided into 12 equal parts, and two hours are used as one section to obtain the section value of the current user login time. And temporarily storing the obtained context information in a database, and entering a user operation module after the acquisition of the context information is completed.
Step 2, an operation module, specifically comprising the following steps:
The operation module is mainly divided into four operation behaviors: a new data operation, an edit data operation, a view data operation and a find sort data operation.
Newly creating data, filling data according to requirements when a user creates the data, storing the data into a database after the data is created successfully, and entering a feature extraction module;
editing data, namely, a user changes original data on the basis of the existing data, and after editing is successful, the data is stored in a database and enters a feature extraction module;
The data checking operation is that a user clicks detailed information of the checked data on the basis of the existing data, and after closing a window, the user enters a feature extraction module;
The searching and sorting data operation is mainly divided into a key word searching and an irrelevant key word searching. Under the condition that a user does not know keywords, initiating a keyword-free ordering request to enter an ordering module; under the condition that the user knows the keywords, searching the keywords, obtaining keyword search results, and transmitting the search results to the ordering module for ordering.
Step 3, a feature extraction module, which specifically comprises the following steps:
The feature extraction is mainly extracted from three operation behaviors of a new data adding operation, a data editing operation and a data viewing operation of a user, and 11 feature attributes are mainly obtained through screening, wherein the feature extraction comprises the following steps: data newly-built time, data save time, data reference other data times, data reference other data weight values, data filling integrity, data name similarity, created data role information, data edit times, data latest edit time, data check times and data latest check time.
Step 4, a sequencing module, which specifically comprises the following steps:
The method mainly comprises the steps that an intuitive fuzzy number ordering in an intuitive fuzzy multi-attribute decision problem is mainly adopted in an ordering module, each piece of data in a database can be used as a decision method for ordering, 11 attributes of each piece of data can be obtained according to the step 3, membership and non-membership in the attributes and a priority relation among the attributes are determined according to different conditions, and an intuitive fuzzy decision matrix is established for collected decision information; then calculating the closeness of the attribute corresponding to the data according to the membership and the non-membership; and finally, calculating the weight vector of the associated attribute by using an intuitional fuzzy priority weighted average operator (IFPWA), further calculating the aggregation result of the bar data, and sequencing the final aggregation result.
When the irrelevant key word searches for the order, the role and the login time of the user are obtained from the context module, the login time is firstly found in the corresponding time period T in fig. 2, and if the time period T is between the time period 5 and the time period 10, the order of the target attributes among 11 attributes in the step 3 is as follows: attribute 6> attribute 7> attribute 1> attribute 9> attribute 4> attribute 3> attribute 11> attribute 8> attribute 10> attribute 2> attribute 5; if the time period T is not between the time period 5 and the time period 10, the target attribute ranking among 11 attributes in the step 3 is, for example: attribute 6> attribute 7> attribute 9> attribute 4> attribute 3> attribute 11> attribute 8> attribute 10> attribute 2> attribute 1> attribute 5; and sequencing each piece of data according to the membership degree and non-membership degree of each attribute of each piece of data, the target attribute sequencing and the intuitionistic fuzzy decision method. It should be noted that the above-mentioned target attribute ranking is only an example, and the specific attribute ranking may be determined according to different user application scenarios.
When the keyword searching and sorting are performed, a data set is obtained according to the filled keywords by adopting an edit distance algorithm, firstly, the keyword similarity rate in the data set is sorted once from high to low, and then the data set with the same similarity rate is sorted by an irrelevant keyword searching and sorting method.
In summary, the embodiment of the invention provides a small amount of representative data characteristics after the user logs in, ensures the accuracy of displaying the data sequence according to the actual requirement of the user, and avoids the process of searching the target data in a large amount of data for a long time by the user.
Example 4:
fig. 5 is a schematic structural diagram of a data sorting device according to an embodiment of the present invention, including:
a first determining module 51, configured to determine, for each piece of data in the system, a membership degree and a non-membership degree of each attribute of the data when a data ordering request is received;
A second determining module 52, configured to determine first target role information and target login time information of a user currently logged in the system, and determine a target attribute ranking according to a correspondence between each login time period and an attribute ranking that is pre-stored for each role information;
The first sorting module 53 is configured to sort each piece of data according to the membership degree and non-membership degree of each attribute of each piece of data, the target attribute sorting and the intuitive fuzzy decision method.
The apparatus further comprises:
the judging module 54 is configured to judge whether the data ordering request carries a keyword, and if not, trigger the first determining module 51.
The apparatus further comprises:
The second sorting module 55 is configured to determine the similarity between each piece of data in the system and the keyword according to the edit distance algorithm, select the target data with the similarity greater than the preset similarity threshold, and sort the target data according to the similarity of the target data.
The judging module 54 is further configured to judge whether there is target data with the same similarity, and if so, trigger the first determining module for each piece of target data with the same similarity.
Example 5:
on the basis of the above embodiments, the embodiment of the present invention further provides an electronic device, as shown in fig. 6, including: processor 301, communication interface 302, memory 303 and communication bus 304, wherein processor 301, communication interface 302, memory 303 complete the communication each other through communication bus 304;
the memory 303 has stored therein a computer program which, when executed by the processor 301, causes the processor 301 to perform the steps of:
when a data ordering request is received, determining the membership degree and non-membership degree of each attribute of each piece of data in the system;
Determining first target role information and target login time information of a user currently logged in the system, and determining target attribute sequencing according to a corresponding relation between each login time period and attribute sequencing, which is pre-stored for each role information;
and sequencing each piece of data according to the membership degree and non-membership degree of each attribute of each piece of data, the target attribute sequencing and the intuitionistic fuzzy decision method.
Based on the same inventive concept, the embodiment of the invention also provides an electronic device, and because the principle of solving the problem of the electronic device is similar to that of the data sorting method, the implementation of the electronic device can refer to the implementation of the method, and the repetition is omitted.
The electronic device provided by the embodiment of the invention can be a desktop computer, a portable computer, a smart phone, a tablet Personal computer, a Personal digital assistant (Personal DIGITAL ASSISTANT, PDA), a network side device and the like.
The communication bus mentioned above for the electronic device may be a peripheral component interconnect standard (PERIPHERAL COMPONENT INTERCONNECT, PCI) bus or an extended industry standard architecture (Extended Industry Standard Architecture, EISA) bus, etc. The communication bus may be classified as an address bus, a data bus, a control bus, or the like. For ease of illustration, the figures are shown with only one bold line, but not with only one bus or one type of bus.
The communication interface 302 is used for communication between the electronic device and other devices described above.
The Memory may include random access Memory (Random Access Memory, RAM) or may include Non-Volatile Memory (NVM), such as at least one disk Memory. Optionally, the memory may also be at least one memory device located remotely from the aforementioned processor.
The processor may be a general-purpose processor, including a central processing unit, a network processor (Network Processor, NP), etc.; but may also be a digital signal processor (DIGITAL SIGNAL Processing unit, DSP), application specific integrated circuit, field programmable gate array or other programmable logic device, discrete gate or transistor logic device, discrete hardware components, or the like.
When the processor executes the program stored in the memory, the method can determine the membership degree and non-membership degree of each attribute of each piece of data in the system after receiving the data ordering request, determine the target attribute ordering according to the first target role information and the target login time information of the user logging in the system currently, and order each piece of data according to the membership degree and non-membership degree of each attribute of each piece of data, the target attribute ordering and the intuitionistic fuzzy decision method. Therefore, the data sorting results corresponding to different users at different login times can be determined, and a data sorting scheme based on the use habit of the users is provided.
Example 6:
On the basis of the above embodiments, the embodiments of the present invention further provide a computer-readable storage medium having stored therein a computer program executable by an electronic device, which when run on the electronic device, causes the electronic device to perform the steps of:
when a data ordering request is received, determining the membership degree and non-membership degree of each attribute of each piece of data in the system;
Determining first target role information and target login time information of a user currently logged in the system, and determining target attribute sequencing according to a corresponding relation between each login time period and attribute sequencing, which is pre-stored for each role information;
and sequencing each piece of data according to the membership degree and non-membership degree of each attribute of each piece of data, the target attribute sequencing and the intuitionistic fuzzy decision method.
Based on the same inventive concept, the embodiment of the present invention further provides a computer readable storage medium, and since the principle of solving the problem when the processor executes the computer program stored on the computer readable storage medium is similar to the data sorting method, the implementation of the processor executing the computer program stored on the computer readable storage medium can refer to the implementation of the method, and the repetition is omitted.
The computer readable storage medium may be any available medium or data storage device that can be accessed by a processor in an electronic device, including but not limited to magnetic memories such as floppy disks, hard disks, magnetic tapes, magneto-optical disks (MO), etc., optical memories such as CD, DVD, BD, HVD, etc., and semiconductor memories such as ROM, EPROM, EEPROM, nonvolatile memories (NAND FLASH), solid State Disks (SSD), etc.
The computer program is stored in the computer readable storage medium provided by the embodiment of the invention, when the computer program is executed by a processor, the membership degree and the non-membership degree of each attribute of each piece of data in a system can be determined after the data ordering request is received, the target attribute ordering is determined according to the first target role information and the target login time information of the user currently logged in the system, and each piece of data is ordered according to the membership degree and the non-membership degree of each attribute of each piece of data, the target attribute ordering and an intuitive fuzzy decision method. Therefore, the data sorting results corresponding to different users at different login times can be determined, and a data sorting scheme based on the use habit of the users is provided.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. It is therefore intended that the following claims be interpreted as including the preferred embodiments and all such alterations and modifications as fall within the scope of the invention.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present invention without departing from the spirit or scope of the invention. Thus, it is intended that the present invention also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.
Claims (9)
1. A method of ordering data, the method comprising:
when a data ordering request is received, determining the membership degree and non-membership degree of each attribute of each piece of data in the system;
if the data attribute is any one of data newly-built time, data saving time, data reference other data times, data reference other data weight values, data editing times, data latest editing time, data checking times and data latest checking time, determining the membership degree of the attribute of the data according to the corresponding relation between the data of the attribute and the membership degree;
If the data attribute is the data filling integrity, determining the filling integrity of the data according to the filling condition of the data, and taking the filling integrity of the data as the membership of the data;
if the data attribute is the data name similarity, determining the name similarity of the data and other data, and taking the highest name similarity as the membership of the data;
If the data attribute is creation data role information, acquiring first target role information of a user currently logged in the system and creating second target role information of the data, and determining membership of the data according to the first target role information and the second target role information; if the first target role information and the second target role information are the same, determining that the membership degree of the data is highest, and if the first target role information and the second target role information are different, the higher the level of the second target role information is, the higher the membership degree of the data is;
Taking the difference value between 1 and the membership degree of the data as the non-membership degree of the data aiming at each data attribute;
Determining first target role information and target login time information of a user currently logged in the system, and determining target attribute sequencing according to a corresponding relation between each login time period and attribute sequencing, which is pre-stored for each role information;
and sequencing each piece of data according to the membership degree and non-membership degree of each attribute of each piece of data, the target attribute sequencing and the intuitionistic fuzzy decision method.
2. The method of claim 1, wherein determining the membership and non-membership of each attribute of the data comprises:
If the data attribute is the data new time, acquiring the new time of the data, and determining the membership of the data according to the new time of the data and the preset corresponding relation between each new time period and the membership;
If the data attribute is data preservation time, acquiring new time of the data, determining preservation time of the data according to the new time and current time of the data, and determining membership of the data according to the preservation time of the data and a preset corresponding relation between each preservation time period and membership;
if the data attribute is the number of times that the data references other data, acquiring the number of times that the data references other data, and determining the membership degree of the data according to the number of times that the data references other data and the preset corresponding relation between each frequency range and the membership degree;
If the data attribute is the weight value of the data reference other data, determining the weight value of the other data of the data according to the preset weight value corresponding to each data, and determining the membership of the data according to the weight value of the data reference other data and the preset corresponding relation between each weight value range and the membership;
If the data attribute is the data editing frequency, determining the editing frequency of the data, and determining the membership of the data according to the editing frequency of the data and the preset corresponding relation between each editing frequency range and the membership;
If the data attribute is the latest editing time of the data, acquiring the latest editing time of the data, and determining the membership of the data according to the latest editing time of the data and the preset corresponding relation between each editing time period and the membership;
if the data attribute is the data checking frequency, acquiring the checking frequency of the data, and determining the membership of the data according to the checking frequency of the data and the preset corresponding relation between each checking frequency range and the membership;
if the data attribute is the latest viewing time of the data, acquiring the latest viewing time of the data, and determining the membership of the data according to the latest viewing time of the data and the preset corresponding relation between each viewing time period and the membership.
3. The method of claim 1, wherein after receiving the data ordering request, before determining membership and non-membership of each attribute of each piece of data in the system for the piece of data, the method further comprises:
judging whether the data ordering request carries a keyword or not, and if not, carrying out the follow-up steps.
4. The method of claim 3, wherein if a key is carried in the data ordering request, the method further comprises:
According to an edit distance algorithm, determining the similarity between each piece of data in the system and the keywords, selecting target data with the similarity larger than a preset similarity threshold, and sorting according to the similarity of the target data.
5. The method of claim 4, wherein the method further comprises:
Judging whether target data with the same similarity exist or not, if so, determining the membership degree and non-membership degree of each attribute of the target data aiming at each piece of target data with the same similarity; and carrying out the subsequent sorting step according to the membership degree and non-membership degree of each attribute of each item of target data.
6. A data ordering apparatus, the apparatus comprising:
the first determining module is used for determining the membership degree and the non-membership degree of each attribute of each piece of data in the system when a data ordering request is received;
if the data attribute is any one of data newly-built time, data saving time, data reference other data times, data reference other data weight values, data editing times, data latest editing time, data checking times and data latest checking time, determining the membership degree of the attribute of the data according to the corresponding relation between the data of the attribute and the membership degree;
If the data attribute is the data filling integrity, determining the filling integrity of the data according to the filling condition of the data, and taking the filling integrity of the data as the membership of the data;
if the data attribute is the data name similarity, determining the name similarity of the data and other data, and taking the highest name similarity as the membership of the data;
If the data attribute is creation data role information, acquiring first target role information of a user currently logged in the system and creating second target role information of the data, and determining membership of the data according to the first target role information and the second target role information; if the first target role information and the second target role information are the same, determining that the membership degree of the data is highest, and if the first target role information and the second target role information are different, the higher the level of the second target role information is, the higher the membership degree of the data is;
Taking the difference value between 1 and the membership degree of the data as the non-membership degree of the data aiming at each data attribute;
The second determining module is used for determining first target role information and target login time information of a user currently logged in the system, and determining target attribute sequencing according to the corresponding relation between each login time period and attribute sequencing, which is pre-stored for each role information;
and the first ordering module is used for ordering each piece of data according to the membership degree and non-membership degree of each attribute of each piece of data, the target attribute ordering and the intuitionistic fuzzy decision method.
7. The apparatus of claim 6, wherein the apparatus further comprises:
The judging module is used for judging whether the data ordering request carries keywords or not, and if not, triggering the first determining module.
8. The electronic equipment is characterized by comprising a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory are communicated with each other through the communication bus;
A memory for storing a computer program;
a processor for implementing the method steps of any one of claims 1-5 when executing a program stored on a memory.
9. A computer-readable storage medium, characterized in that the computer-readable storage medium has stored therein a computer program which, when executed by a processor, implements the method steps of any of claims 1-5.
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